Finding cures for diseases is by its very nature a complicated, expensive, and prolonged business filled with risk and uncertainty. Pharmaceutical and biotechnology companies initiate hundreds of clinical trials and invest millions in discovering a drug or device and bringing it to market. Anything that can accelerate the process, without compromising quality and safety, is a primary concern for these companies.

But most clinical trials face significant delays due to challenges in finding and recruiting patients who meet the trial’s eligibility criteria – a sixth of all trials take twice as much time to enroll patients as initially planned. These delays in recruitment can cost companies dearly. It is estimated that a single day’s delay in patient enrolment during the advanced stages of a trial can set back a company by more than thirty-seven thousand dollars in operational expenses and over one million dollars in opportunity costs. On the flip side, shrinking the duration of a trial by just thirty days can lead to an increase of forty million dollarsin sales revenue for a newly launched drug. Moreover, average patient retention rates for clinical trials are as low as thirty-percent which further escalates costs.

Clinical research ecosystems today have more data at their disposal than ever before, from patient medical records to consent data to data on health outcomes, making patient recruitment and retention a classic problem that can be addressed with data science. Using this massive repository of data can revolutionize clinical trials from curbing costly delays to securing drug approvals on time. A study by Clinical Trials Transformation Initiative’s (CTTI) Recruitment Project revealed that significant recruitment challenges can indeed be addressed through data analytics:

Identifying patients who meet the trial eligibility criteria (chosen by 81% of participants)

Inadequate personnel time for enrolment (chosen by 67% of participants)

Analysis of lengthy and complex consent forms (chosen by 66% of participants)

We recently did just this for a leading research organization based out of the United States by helping them harness the power of data and applying predictive analytics. Using ShareInsights, our self-service big data analytics platform, researchers at the organization analyzed terabytes of data such as patient data, prognosis data, diseases data, treatment data, hospital data, trial protocols, and consent information to determine patient eligibility rapidly. Point and click machine learning algorithms helped them predict dropout propensity and reduce the dropout rate by a whopping 54%. We’ve put together a case study that describes how the organization increased patient recruitment numbers by 38% due to faster identification of eligible patients and reduced trial delays by 30%. Read the complete case study here.